Results 111 to 120 of about 25,829 (292)

A knowledge distillation strategy for enhancing the adversarial robustness of lightweight automatic modulation classification models

IET Communications
Automatic modulation classification models based on deep learning models are at risk of being interfered by adversarial attacks. In an adversarial attack, the attacker causes the classification model to misclassify the received signal by adding carefully
Fanghao Xu, Chao Wang, Jiakai Liang, Chenyang Zuo, Keqiang Yue, Wenjun Li   +5 more
doaj   +1 more source

Pareto Adversarial Robustness: Balancing Spatial Robustness and Sensitivity-based Robustness

Adversarial robustness, which primarily comprises sensitivity-based robustness and spatial robustness, plays an integral part in achieving robust generalization. In this paper, we endeavor to design strategies to achieve universal adversarial robustness.
Sun, Ke, Li, Mingjie, Lin, Zhouchen
core   +1 more source

PlantGFM: A Genomic Foundation Model for Discovery and Creation of Plant Genes

Advanced Science, EarlyView.
A plant genomic foundation model pre‐trained on 12 species enables both accurate gene prediction and de novo gene design. Through AI‐human knowledge screening, seven designed sequences showed transcriptional activity in plants, with two expressing stable proteins—demonstrating the first DNA‐RNA‐protein expression of LLM‐generated genes in plants and ...
Changhao Li, Qizhe Zhang, Hanchen Chen, Kepeng Lin, Chengfang Luo, Mengying Yang, Wei Xu, Fan Yao, Jianbing Yan, Qing‐Yong Yang, Xuehai Hu   +10 more
wiley   +1 more source

Knowing is Half the Battle: Enhancing Clean Data Accuracy of Adversarial Robust Deep Neural Networks via Dual-Model Bounded Divergence Gating

IEEE Access
Significant advances have been made in recent years in improving the robustness of deep neural networks, particularly under adversarial machine learning scenarios where the data has been contaminated to fool networks into making undesirable predictions ...
Hossein Aboutalebi, Mohammad Javad Shafiee, Chi-En Amy Tai, Alexander Wong   +3 more
doaj   +1 more source

The Impact of Simultaneous Adversarial Attacks on Robustness of Medical Image Analysis

Deep learning models are widely used in healthcare systems. However, deep learning models are vulnerable to attacks themselves. Significantly, due to the black-box nature of the deep learning model, it is challenging to detect attacks.
Rahman, Saifur, Habib, Ahsan, Jadidi, Zahra, Karmakar, Chandan, Beheshti, Maedeh, Pal, Shantanu   +5 more
core   +1 more source

How Advanced Artificial Intelligence Technologies Shape Drug–Drug and Drug–Target Interaction Modeling

Advanced Science, EarlyView.
This review explores the convergence of artificial intelligence technologies in modeling drug–drug and drug–target interactions. By evaluating advanced feature engineering, architectural innovations, and learning paradigms reveals shared evolutionary trends and critical challenges, such as cold‐start settings and shortcut learning.
Xin Sun, Tong Wang
wiley   +1 more source

Bit flipping-based error correcting output code construction for adversarial robustness of neural networks

ICT Express
In this paper, we propose a method for constructing error-correcting output codes (ECOCs) based on a codeword bit flipping algorithm to enhance adversarial robustness of neural networks.
Wooram Jang, Woojin Hwang, Kezhong Jin, Hosung Park   +3 more
doaj   +1 more source

An Extended Study of Human-like Behavior under Adversarial Training

, 2023
Neural networks have a number of shortcomings. Amongst the severest ones is the sensitivity to distribution shifts which allows models to be easily fooled into wrong predictions by small perturbations to inputs that are often imperceivable to humans and ...
Gavrikov, Paul, Keuper, Margret, Keuper, Janis   +2 more
core   +1 more source

AI‐Physics‐Experiment Trinity for Integrated Protein Dynamics Modeling

Advanced Science, EarlyView.
This review unites experiments, physics‐based simulations, and AI as a synergistic triad for protein dynamics modeling. It highlights integrative strategies, resolves sampling and forcefield bottlenecks, and outlines challenges and future directions for accurate, interpretable conformational ensemble prediction.
Chen Shi, Minying Low, Peng Xiu, Kresten Lindorff‐Larsen, Yong Wang   +4 more
wiley   +1 more source

SQUEEZE TRAINING FOR ADVERSARIAL ROBUSTNESS

, 2023
The vulnerability of deep neural networks (DNNs) to adversarial examples has attracted great attention in the machine learning community. The problem is related to non-flatness and non-smoothness of normally obtained loss landscapes.
Zuo, Wangmeng, Guo, Yiwen, Li, Qizhang, Chen, Hao   +3 more
core   +1 more source